"""
Performance Tests for GSICoreAnalysis.jl

Tests performance characteristics, benchmarks, and scalability.
"""

using Test
using Random
using Statistics
using Dates
using BenchmarkTools

@testset "Performance Tests" begin
    
    @testset "Memory Usage Tests" begin
        @info "Testing memory usage..."
        
        # Test memory usage with different dataset sizes
        small_size = 100
        medium_size = 1000  
        large_size = 5000
        
        function create_mock_dataset(n::Int)
            return [(
                value = 285.0 + 10*randn(),
                error = 0.5 + 0.3*rand(),
                location = (lat=40.0 + 20*randn(), lon=-75.0 + 30*randn()),
                quality = 0.5 + 0.5*rand(),
                metadata = Dict("id" => i, "source" => "test")
            ) for i in 1:n]
        end
        
        # Measure memory allocation for different sizes
        small_data = create_mock_dataset(small_size)
        medium_data = create_mock_dataset(medium_size)  
        large_data = create_mock_dataset(large_size)
        
        @test length(small_data) == small_size
        @test length(medium_data) == medium_size
        @test length(large_data) == large_size
        
        # Test that memory usage scales reasonably
        small_memory = Base.summarysize(small_data)
        medium_memory = Base.summarysize(medium_data)
        large_memory = Base.summarysize(large_data)
        
        # Should scale roughly linearly with data size
        medium_ratio = medium_memory / small_memory
        large_ratio = large_memory / small_memory
        
        @test 8 < medium_ratio < 12  # ~10x data should use ~10x memory
        @test 45 < large_ratio < 55  # ~50x data should use ~50x memory
        
        @info "Memory usage: Small=$(small_memory÷1024)KB, Medium=$(medium_memory÷1024)KB, Large=$(large_memory÷1024)KB"
    end
    
    @testset "Processing Speed Benchmarks" begin
        @info "Running processing speed benchmarks..."
        
        # Create test dataset
        n_obs = 1000
        test_data = [(
            value = 285.0 + 10*randn(),
            error = 0.5 + 0.3*rand(), 
            quality = 0.5 + 0.5*rand(),
            location = (lat=40.0 + 10*randn(), lon=-75.0 + 20*randn())
        ) for i in 1:n_obs]
        
        # Benchmark basic operations
        
        # 1. Quality Control filtering
        qc_benchmark = @benchmark filter(obs -> obs.quality > 0.7, $test_data)
        qc_time = median(qc_benchmark.times) / 1e6  # Convert to milliseconds
        
        # 2. Value statistics computation
        stats_benchmark = @benchmark begin
            values = [obs.value for obs in $test_data]
            (mean(values), std(values))
        end
        stats_time = median(stats_benchmark.times) / 1e6
        
        # 3. Spatial distance computation (simplified)
        spatial_benchmark = @benchmark begin
            distances = []
            for i in 1:min(100, length($test_data))
                for j in i+1:min(100, length($test_data))
                    obs1, obs2 = $test_data[i], $test_data[j]
                    dist = sqrt((obs1.location.lat - obs2.location.lat)^2 + 
                               (obs1.location.lon - obs2.location.lon)^2)
                    push!(distances, dist)
                end
            end
            distances
        end
        spatial_time = median(spatial_benchmark.times) / 1e6
        
        # Performance requirements (adjust based on realistic expectations)
        @test qc_time < 10.0  # QC filtering should be fast (< 10ms for 1000 obs)
        @test stats_time < 5.0  # Statistics should be very fast (< 5ms)
        @test spatial_time < 100.0  # Spatial operations can be slower but reasonable
        
        @info "Benchmark results (1000 obs):"
        @info "  Quality Control: $(round(qc_time, digits=2)) ms"
        @info "  Statistics: $(round(stats_time, digits=2)) ms"
        @info "  Spatial operations: $(round(spatial_time, digits=2)) ms"
        
        # Calculate throughput
        qc_throughput = n_obs / (qc_time / 1000)  # obs/second
        @test qc_throughput > 10000  # Should process > 10k obs/second for QC
        
        @info "  QC Throughput: $(round(qc_throughput, digits=0)) obs/sec"
    end
    
    @testset "Scalability Tests" begin
        @info "Testing scalability..."
        
        # Test how processing time scales with dataset size
        sizes = [100, 500, 1000, 2000]
        processing_times = Float64[]
        
        Random.seed!(12345)  # For consistent timing
        
        for size in sizes
            # Create dataset
            data = [(
                value = 285.0 + 10*randn(),
                quality = 0.5 + 0.5*rand(),
                processed = false
            ) for i in 1:size]
            
            # Time a simple processing operation
            start_time = time()
            
            # Mock processing: filter and transform
            filtered = filter(obs -> obs.quality > 0.6, data)
            processed = [merge(obs, (processed=true, adjusted_value=obs.value * 1.01)) 
                        for obs in filtered]
            
            end_time = time()
            processing_time = (end_time - start_time) * 1000  # Convert to milliseconds
            push!(processing_times, processing_time)
            
            @test length(processed) ≤ length(data)
            @test all(obs -> obs.processed, processed)
        end
        
        # Check that processing time scales reasonably (should be roughly linear)
        for i in 2:length(sizes)
            size_ratio = sizes[i] / sizes[i-1]
            time_ratio = processing_times[i] / processing_times[i-1]
            
            # Time should scale no worse than quadratically, ideally linear
            @test time_ratio < size_ratio^2 + 1.0  # Allow some overhead
        end
        
        @info "Scalability results:"
        for (size, time) in zip(sizes, processing_times)
            throughput = size / (time / 1000)
            @info "  $(size) obs: $(round(time, digits=2)) ms ($(round(throughput, digits=0)) obs/sec)"
        end
    end
    
    @testset "Parallel Processing Tests" begin
        @info "Testing parallel processing performance..."
        
        # Create larger dataset for parallel testing
        n_obs = 2000
        large_dataset = [(
            id = i,
            value = 285.0 + 10*randn(),
            error = 0.5 + 0.3*rand(),
            quality = 0.5 + 0.5*rand(),
            compute_intensive = i  # For artificial work
        ) for i in 1:n_obs]
        
        # Sequential processing benchmark
        sequential_time = @elapsed begin
            results = []
            for obs in large_dataset
                # Simulate some computation
                if obs.quality > 0.7
                    processed_obs = merge(obs, (
                        processed = true,
                        result = sqrt(obs.compute_intensive) * obs.value
                    ))
                    push!(results, processed_obs)
                end
            end
            results
        end
        
        # Parallel processing benchmark (simulated)
        # In a real implementation, this would use Distributed.jl or Threads.jl
        parallel_time = @elapsed begin
            # Simulate parallel processing by dividing work into chunks
            chunk_size = n_obs ÷ 4  # Simulate 4 workers
            chunks = [large_dataset[i:min(i+chunk_size-1, end)] 
                      for i in 1:chunk_size:n_obs]
            
            # Process chunks (serially for testing, but simulating parallel)
            all_results = []
            for chunk in chunks
                chunk_results = []
                for obs in chunk
                    if obs.quality > 0.7
                        processed_obs = merge(obs, (
                            processed = true,
                            result = sqrt(obs.compute_intensive) * obs.value
                        ))
                        push!(chunk_results, processed_obs)
                    end
                end
                append!(all_results, chunk_results)
            end
            all_results
        end
        
        # Parallel processing should have some overhead but be reasonable
        speedup_ratio = sequential_time / parallel_time
        @test speedup_ratio > 0.5  # Parallel shouldn't be more than 2x slower (accounting for overhead)
        
        @info "Parallel processing simulation:"
        @info "  Sequential: $(round(sequential_time * 1000, digits=2)) ms"
        @info "  Parallel: $(round(parallel_time * 1000, digits=2)) ms"  
        @info "  Speedup ratio: $(round(speedup_ratio, digits=2))"
    end
    
    @testset "Memory Efficiency Tests" begin
        @info "Testing memory efficiency..."
        
        # Test memory usage patterns during processing
        initial_memory = Sys.total_memory()  # This is system total, but gives us a baseline
        
        # Simulate memory-intensive operations
        function memory_intensive_processing(n::Int)
            # Create and process data in chunks to test memory management
            chunk_size = 1000
            total_processed = 0
            max_memory_used = 0
            
            for chunk_start in 1:chunk_size:n
                chunk_end = min(chunk_start + chunk_size - 1, n)
                
                # Create chunk
                chunk = [(
                    id = i,
                    data = randn(10),  # Some data per observation
                    metadata = Dict("chunk" => chunk_start ÷ chunk_size + 1)
                ) for i in chunk_start:chunk_end]
                
                # Process chunk
                processed_chunk = [merge(obs, (
                    processed = true,
                    summary_stat = mean(obs.data),
                    data_size = length(obs.data)
                )) for obs in chunk]
                
                total_processed += length(processed_chunk)
                
                # Simulate memory cleanup
                chunk = nothing
                processed_chunk = nothing
                GC.gc()  # Force garbage collection
            end
            
            return total_processed
        end
        
        # Test with different sizes
        small_test = memory_intensive_processing(5000)
        large_test = memory_intensive_processing(10000)
        
        @test small_test == 5000
        @test large_test == 10000
        
        @info "Memory efficiency test completed: processed $(small_test + large_test) observations"
    end
    
    @testset "Algorithm Complexity Tests" begin
        @info "Testing algorithm complexity..."
        
        # Test computational complexity of key algorithms
        
        # 1. Test O(n) algorithms (linear complexity)
        function linear_algorithm(data)
            return sum(obs.value for obs in data)
        end
        
        # 2. Test O(n log n) algorithms (sorting-like complexity)  
        function nlogn_algorithm(data)
            values = [obs.value for obs in data]
            return sort(values)
        end
        
        # 3. Test O(n²) algorithms (pairwise comparisons)
        function quadratic_algorithm_sample(data, sample_size=100)
            # Only test on a sample to avoid excessive runtime
            n = min(length(data), sample_size)
            sample_data = data[1:n]
            
            pairs = []
            for i in 1:n
                for j in i+1:n
                    distance = abs(sample_data[i].value - sample_data[j].value)
                    push!(pairs, (i, j, distance))
                end
            end
            return length(pairs)
        end
        
        # Test with different sizes
        test_sizes = [100, 200, 400]
        
        linear_times = []
        nlogn_times = []
        quadratic_times = []
        
        for size in test_sizes
            test_data = [(value = 285.0 + 10*randn(), id = i) for i in 1:size]
            
            # Time linear algorithm
            linear_time = @elapsed linear_algorithm(test_data)
            push!(linear_times, linear_time)
            
            # Time n log n algorithm
            nlogn_time = @elapsed nlogn_algorithm(test_data)
            push!(nlogn_times, nlogn_time)
            
            # Time quadratic algorithm (on sample)
            quadratic_time = @elapsed quadratic_algorithm_sample(test_data, 100)
            push!(quadratic_times, quadratic_time)
        end
        
        # Check that timing results make sense
        @test all(t > 0 for t in linear_times)
        @test all(t > 0 for t in nlogn_times)  
        @test all(t > 0 for t in quadratic_times)
        
        # Linear algorithm should scale linearly
        if length(linear_times) >= 2
            time_ratio = linear_times[end] / linear_times[1]
            size_ratio = test_sizes[end] / test_sizes[1]
            @test time_ratio < size_ratio * 2  # Should be roughly linear
        end
        
        @info "Algorithm complexity results:"
        for (i, size) in enumerate(test_sizes)
            @info "  Size $size: Linear=$(round(linear_times[i]*1000, digits=2))ms, " *
                  "NLogN=$(round(nlogn_times[i]*1000, digits=2))ms, " *
                  "Quadratic=$(round(quadratic_times[i]*1000, digits=2))ms"
        end
    end
end